Artificial Intelligence Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Breed Classifier


Introduction

In this notebook, I make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, this code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of the finished project.

Sample Dog Output

In this real-world setting, we will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. The imperfect solution here will nonetheless create a fun user experience!

The Road Ahead

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write Own Algorithm
  • Step 7: Test Own Algorithm

Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels

Dog dataset https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip

In [3]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames']) #the name of the file in string
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133) # one-hot encoding of the label
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using TensorFlow backend.
There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.
In [3]:
train_files
Out[3]:
array(['dogImages/train\\095.Kuvasz\\Kuvasz_06442.jpg',
       'dogImages/train\\057.Dalmatian\\Dalmatian_04054.jpg',
       'dogImages/train\\088.Irish_water_spaniel\\Irish_water_spaniel_06014.jpg',
       ..., 'dogImages/train\\029.Border_collie\\Border_collie_02069.jpg',
       'dogImages/train\\046.Cavalier_king_charles_spaniel\\Cavalier_king_charles_spaniel_03261.jpg',
       'dogImages/train\\048.Chihuahua\\Chihuahua_03416.jpg'],
      dtype='<U99')
In [4]:
train_targets
Out[4]:
array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       ..., 
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])
In [4]:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline

def visualize_img(img_path, ax):
    img = cv2.imread(img_path) # show images in color
    ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    
fig = plt.figure(figsize=(20, 10))
for i in range(12):
    ax = fig.add_subplot(3, 4, i + 1, xticks=[], yticks=[])
    visualize_img(train_files[i], ax)

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files. Human dataset https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip

In [5]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.
In [6]:
human_files[3]
Out[6]:
'lfw\\Laurence_Fishburne\\Laurence_Fishburne_0001.jpg'
In [7]:
fig = plt.figure(figsize=(20, 10))
for i in range(12):
    ax = fig.add_subplot(3, 4, i + 1, xticks=[], yticks=[])
    visualize_img(human_files[i], ax)

Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [8]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye_tree_eyeglasses.xml')

for i in range(12):
    # load color (BGR) image
    img = cv2.imread(human_files[i])
    # convert BGR image to grayscale
    
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # find faces in image
    faces = face_cascade.detectMultiScale(gray)

    # find eyes in image
    eyes = eye_cascade.detectMultiScale(gray)

    # print number of faces detected in the image
    print('Number of faces detected:', len(faces))

    # print number of eye detected in the image
    print('Number of eyes detected:', len(eyes))

    # get bounding box for each detected face
    for (x,y,w,h) in faces:
        # add bounding box to color image
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

    # show eyes
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(img,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

    # convert BGR image to RGB for plotting
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # display the image, along with bounding box
    plt.imshow(cv_rgb)
    plt.show()
Number of faces detected: 0
Number of eyes detected: 0
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 0
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 1
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 1
Number of faces detected: 1
Number of eyes detected: 2
Number of faces detected: 1
Number of eyes detected: 0
Number of faces detected: 1
Number of eyes detected: 2
In [9]:
print (faces)
[[76 74 98 98]]

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [10]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. Our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

  • Percentage of the first 100 images in human_files have a detected human face: 99%
  • Percentage of the first 100 images in dog_files have a detected human face: 11%
  • As we can see from the failure detection, if the human does not show front face, it is difficult to detect human in human images. For dogs detected as human, some have both human and dog in the images, and some dogs may be mistaken as human face.
In [12]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
def face_detector_performance(files):   
    true_count = 0
    for file in files:
        if face_detector(file): 
            if file.startswith('dogImages'): 
                print ("-----------Detect human face in dog-----------")
                fig = plt.figure(figsize=(20, 10))
                ax = fig.add_subplot(3, 4, 1, xticks=[], yticks=[])
                visualize_img(file, ax)
                plt.show()
            true_count += 1
        else:
            if file.startswith('lfw'): 
                print ("-----------Fail to detect human face in human-----------")
                fig = plt.figure(figsize=(20, 10))
                ax = fig.add_subplot(3, 4, 1, xticks=[], yticks=[])
                visualize_img(file, ax)
                plt.show()
    return 1.*true_count/len(files)

human_perf = face_detector_performance(human_files_short)
dog_perf = face_detector_performance(dog_files_short)

print ("Percentage of the first 100 images in human_files have a detected human face: {0:.2f}%".format(100*human_perf))
print ("Percentage of the first 100 images in dog_files have a detected human face: {0:.2f}%".format(100*dog_perf))
-----------Fail to detect human face in human-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
-----------Detect human face in dog-----------
Percentage of the first 100 images in human_files have a detected human face: 99.00%
Percentage of the first 100 images in dog_files have a detected human face: 11.00%

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer: I think users may get frustrated if we set the standard of face images too strict. This is not quite a reasonable expectation to pose on the user. Therefore, we can train our own dataset on human faces using ImageNet dataset and transfer learning.


Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [13]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
In [39]:
ResNet50_model.summary()
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 224, 224, 3)   0                                            
____________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D) (None, 230, 230, 3)   0                                            
____________________________________________________________________________________________________
conv1 (Conv2D)                   (None, 112, 112, 64)  9472                                         
____________________________________________________________________________________________________
bn_conv1 (BatchNormalization)    (None, 112, 112, 64)  256                                          
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 112, 112, 64)  0                                            
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2a_branch2a (Conv2D)          (None, 55, 55, 64)    4160                                         
____________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2a_branch2b (Conv2D)          (None, 55, 55, 64)    36928                                        
____________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_3 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2a_branch2c (Conv2D)          (None, 55, 55, 256)   16640                                        
____________________________________________________________________________________________________
res2a_branch1 (Conv2D)           (None, 55, 55, 256)   16640                                        
____________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024                                         
____________________________________________________________________________________________________
bn2a_branch1 (BatchNormalization (None, 55, 55, 256)   1024                                         
____________________________________________________________________________________________________
add_1 (Add)                      (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
activation_4 (Activation)        (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
res2b_branch2a (Conv2D)          (None, 55, 55, 64)    16448                                        
____________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_5 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2b_branch2b (Conv2D)          (None, 55, 55, 64)    36928                                        
____________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_6 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2b_branch2c (Conv2D)          (None, 55, 55, 256)   16640                                        
____________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024                                         
____________________________________________________________________________________________________
add_2 (Add)                      (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
activation_7 (Activation)        (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
res2c_branch2a (Conv2D)          (None, 55, 55, 64)    16448                                        
____________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_8 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2c_branch2b (Conv2D)          (None, 55, 55, 64)    36928                                        
____________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizatio (None, 55, 55, 64)    256                                          
____________________________________________________________________________________________________
activation_9 (Activation)        (None, 55, 55, 64)    0                                            
____________________________________________________________________________________________________
res2c_branch2c (Conv2D)          (None, 55, 55, 256)   16640                                        
____________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024                                         
____________________________________________________________________________________________________
add_3 (Add)                      (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
activation_10 (Activation)       (None, 55, 55, 256)   0                                            
____________________________________________________________________________________________________
res3a_branch2a (Conv2D)          (None, 28, 28, 128)   32896                                        
____________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_11 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3a_branch2b (Conv2D)          (None, 28, 28, 128)   147584                                       
____________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_12 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3a_branch2c (Conv2D)          (None, 28, 28, 512)   66048                                        
____________________________________________________________________________________________________
res3a_branch1 (Conv2D)           (None, 28, 28, 512)   131584                                       
____________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048                                         
____________________________________________________________________________________________________
bn3a_branch1 (BatchNormalization (None, 28, 28, 512)   2048                                         
____________________________________________________________________________________________________
add_4 (Add)                      (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
activation_13 (Activation)       (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
res3b_branch2a (Conv2D)          (None, 28, 28, 128)   65664                                        
____________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_14 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3b_branch2b (Conv2D)          (None, 28, 28, 128)   147584                                       
____________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_15 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3b_branch2c (Conv2D)          (None, 28, 28, 512)   66048                                        
____________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048                                         
____________________________________________________________________________________________________
add_5 (Add)                      (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
activation_16 (Activation)       (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
res3c_branch2a (Conv2D)          (None, 28, 28, 128)   65664                                        
____________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_17 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3c_branch2b (Conv2D)          (None, 28, 28, 128)   147584                                       
____________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_18 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3c_branch2c (Conv2D)          (None, 28, 28, 512)   66048                                        
____________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048                                         
____________________________________________________________________________________________________
add_6 (Add)                      (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
activation_19 (Activation)       (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
res3d_branch2a (Conv2D)          (None, 28, 28, 128)   65664                                        
____________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_20 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3d_branch2b (Conv2D)          (None, 28, 28, 128)   147584                                       
____________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizatio (None, 28, 28, 128)   512                                          
____________________________________________________________________________________________________
activation_21 (Activation)       (None, 28, 28, 128)   0                                            
____________________________________________________________________________________________________
res3d_branch2c (Conv2D)          (None, 28, 28, 512)   66048                                        
____________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048                                         
____________________________________________________________________________________________________
add_7 (Add)                      (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
activation_22 (Activation)       (None, 28, 28, 512)   0                                            
____________________________________________________________________________________________________
res4a_branch2a (Conv2D)          (None, 14, 14, 256)   131328                                       
____________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_23 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4a_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_24 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4a_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
res4a_branch1 (Conv2D)           (None, 14, 14, 1024)  525312                                       
____________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
bn4a_branch1 (BatchNormalization (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_8 (Add)                      (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_25 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res4b_branch2a (Conv2D)          (None, 14, 14, 256)   262400                                       
____________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_26 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4b_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_27 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4b_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_9 (Add)                      (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_28 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res4c_branch2a (Conv2D)          (None, 14, 14, 256)   262400                                       
____________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_29 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4c_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_30 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4c_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_10 (Add)                     (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_31 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res4d_branch2a (Conv2D)          (None, 14, 14, 256)   262400                                       
____________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_32 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4d_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_33 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4d_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_11 (Add)                     (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_34 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res4e_branch2a (Conv2D)          (None, 14, 14, 256)   262400                                       
____________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_35 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4e_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_36 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4e_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_12 (Add)                     (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_37 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res4f_branch2a (Conv2D)          (None, 14, 14, 256)   262400                                       
____________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_38 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4f_branch2b (Conv2D)          (None, 14, 14, 256)   590080                                       
____________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024                                         
____________________________________________________________________________________________________
activation_39 (Activation)       (None, 14, 14, 256)   0                                            
____________________________________________________________________________________________________
res4f_branch2c (Conv2D)          (None, 14, 14, 1024)  263168                                       
____________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096                                         
____________________________________________________________________________________________________
add_13 (Add)                     (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
activation_40 (Activation)       (None, 14, 14, 1024)  0                                            
____________________________________________________________________________________________________
res5a_branch2a (Conv2D)          (None, 7, 7, 512)     524800                                       
____________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_41 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5a_branch2b (Conv2D)          (None, 7, 7, 512)     2359808                                      
____________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_42 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5a_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624                                      
____________________________________________________________________________________________________
res5a_branch1 (Conv2D)           (None, 7, 7, 2048)    2099200                                      
____________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192                                         
____________________________________________________________________________________________________
bn5a_branch1 (BatchNormalization (None, 7, 7, 2048)    8192                                         
____________________________________________________________________________________________________
add_14 (Add)                     (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
activation_43 (Activation)       (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
res5b_branch2a (Conv2D)          (None, 7, 7, 512)     1049088                                      
____________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_44 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5b_branch2b (Conv2D)          (None, 7, 7, 512)     2359808                                      
____________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_45 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5b_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624                                      
____________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192                                         
____________________________________________________________________________________________________
add_15 (Add)                     (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
activation_46 (Activation)       (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
res5c_branch2a (Conv2D)          (None, 7, 7, 512)     1049088                                      
____________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_47 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5c_branch2b (Conv2D)          (None, 7, 7, 512)     2359808                                      
____________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048                                         
____________________________________________________________________________________________________
activation_48 (Activation)       (None, 7, 7, 512)     0                                            
____________________________________________________________________________________________________
res5c_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624                                      
____________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192                                         
____________________________________________________________________________________________________
add_16 (Add)                     (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
activation_49 (Activation)       (None, 7, 7, 2048)    0                                            
____________________________________________________________________________________________________
avg_pool (AveragePooling2D)      (None, 1, 1, 2048)    0                                            
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 2048)          0                                            
____________________________________________________________________________________________________
fc1000 (Dense)                   (None, 1000)          2049000                                      
====================================================================================================
Total params: 25,636,712.0
Trainable params: 25,583,592.0
Non-trainable params: 53,120.0
____________________________________________________________________________________________________

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in the dataset!

In [14]:
from keras.preprocessing import image                  
from tqdm import tqdm 
# Fast, Extensible Progress Meter

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type PIL,Python Imaging Library
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [15]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, I notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [16]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

  • Percentage of the first 100 images in human_files have a detected dog face: 1%
  • Percentage of the first 100 images in dog_files have a detected dog face: 100%
  • Dog detector performs quite well in detecting dog face in dogs; it has 1% false positive in detecting dog face in human. In this image, as we can see, the human has white hair.
In [17]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
def dog_detector_performance(files):   
    true_count = 0
    for file in files:
        if dog_detector(file): 
            if file.startswith('lfw'): 
                print ("-----------Detect dog face in human-----------")
                fig = plt.figure(figsize=(20, 10))
                ax = fig.add_subplot(3, 4, 1, xticks=[], yticks=[])
                visualize_img(file, ax)
                plt.show()
            true_count += 1
        else:
            if file.startswith('dogImages'): 
                print ("-----------Fail to detect dog face in dog-----------")
                fig = plt.figure(figsize=(20, 10))
                ax = fig.add_subplot(3, 4, 1, xticks=[], yticks=[])
                visualize_img(file, ax)
                plt.show()
    return 1.*true_count/len(files)

human_perf = dog_detector_performance(human_files_short)
dog_perf = dog_detector_performance(dog_files_short)

print ("Percentage of the first 100 images in human_files have a detected dog face: {0:.2f}%".format(100*human_perf))
print ("Percentage of the first 100 images in dog_files have a detected dog face: {0:.2f}%".format(100*dog_perf))
-----------Detect dog face in human-----------
Percentage of the first 100 images in human_files have a detected dog face: 1.00%
Percentage of the first 100 images in dog_files have a detected dog face: 100.00%

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, I will create a CNN that classifies dog breeds. I create my CNN from scratch, and attain a test accuracy of at least 1%. In Step 5 of this notebook, I will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! Keras provides a handy estimate of the time that each epoch is likely to take.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. My vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

In [18]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6680/6680 [00:44<00:00, 149.47it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 835/835 [00:08<00:00, 99.42it/s]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 836/836 [00:10<00:00, 106.20it/s]
In [19]:
# print shape of training set
print('train_tensors:', train_tensors.shape)

# print number of training, validation, and test images
print(train_tensors.shape[0], 'train samples')
print(test_tensors.shape[0], 'test samples')
print(valid_tensors.shape[0], 'validation samples')
train_tensors: (6680, 224, 224, 3)
6680 train samples
836 test samples
835 validation samples

Model Architecture

Create a CNN to classify dog breed. At the end of the code cell block, summarize the layers of the model by executing the line:

    model.summary()

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer: I first add a convolutional layer with 16 filters of filter size of 3, to take in the input of file image size 224, 224, 3. And then I reduce the dimension of layers using pooling layer with size of 2. I then add another 2 convolutional layers with pooling layers to capture different features of the image. Then using Dropout, I am able to randomly remove the nodes during training. In the end, I flatten the matrix into vector and add 2 fully connected layers to train more specific features, and output 133 nodes for the 133 breeds. Using activation "softmax", I can get the probability of each breed. This architecture works fine with accuracy of 7.7751%, which is much better than random guessing 1/133.

In [20]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential

model = Sequential()

### TODO: Define your architecture.
model.add(Conv2D(filters=16, kernel_size=3, padding='same', activation='relu', 
                        input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(133, activation='softmax'))

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 16)      448       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 32)      4640      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 64)        18496     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 50176)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               25690624  
_________________________________________________________________
dropout_2 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               68229     
=================================================================
Total params: 25,782,437.0
Trainable params: 25,782,437.0
Non-trainable params: 0.0
_________________________________________________________________

Compile the Model

In [21]:
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

Train the Model

Train the model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

In [ ]:
from keras.callbacks import ModelCheckpoint  
import time
### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.
t0 = time.time()
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=2)

t = time.time()-t0
print ("run time: ",t)

Load the Model with the Best Validation Loss

In [61]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Ensure that the test accuracy is greater than 1%.

In [62]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 7.7751%

Step 4: Use a CNN to Classify Dog Breeds

In the following step, I will get a chance to use transfer learning to train my own CNN.

Obtain Bottleneck Features

In [63]:
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

In [64]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229.0
Trainable params: 68,229.0
Non-trainable params: 0.0
_________________________________________________________________

Compile the Model

In [65]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

In [66]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=2)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
Epoch 00000: val_loss improved from inf to 11.83468, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 12.8192 - acc: 0.1099 - val_loss: 11.8347 - val_acc: 0.1713
Epoch 2/20
Epoch 00001: val_loss improved from 11.83468 to 11.14461, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 11.1448 - acc: 0.2287 - val_loss: 11.1446 - val_acc: 0.2335
Epoch 3/20
Epoch 00002: val_loss improved from 11.14461 to 10.85407, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 10.5383 - acc: 0.2909 - val_loss: 10.8541 - val_acc: 0.2431
Epoch 4/20
Epoch 00003: val_loss improved from 10.85407 to 10.53045, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 10.2174 - acc: 0.3202 - val_loss: 10.5305 - val_acc: 0.2874
Epoch 5/20
Epoch 00004: val_loss improved from 10.53045 to 10.39597, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 10.0633 - acc: 0.3455 - val_loss: 10.3960 - val_acc: 0.2934
Epoch 6/20
Epoch 00005: val_loss improved from 10.39597 to 10.35483, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.9386 - acc: 0.3567 - val_loss: 10.3548 - val_acc: 0.3030
Epoch 7/20
Epoch 00006: val_loss improved from 10.35483 to 10.32732, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.8598 - acc: 0.3705 - val_loss: 10.3273 - val_acc: 0.3066
Epoch 8/20
Epoch 00007: val_loss improved from 10.32732 to 10.23063, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.8062 - acc: 0.3751 - val_loss: 10.2306 - val_acc: 0.3114
Epoch 9/20
Epoch 00008: val_loss improved from 10.23063 to 9.92146, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.5059 - acc: 0.3879 - val_loss: 9.9215 - val_acc: 0.3234
Epoch 10/20
Epoch 00009: val_loss improved from 9.92146 to 9.91785, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.3038 - acc: 0.4067 - val_loss: 9.9178 - val_acc: 0.3317
Epoch 11/20
Epoch 00010: val_loss improved from 9.91785 to 9.78630, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.2284 - acc: 0.4147 - val_loss: 9.7863 - val_acc: 0.3281
Epoch 12/20
Epoch 00011: val_loss improved from 9.78630 to 9.73069, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.1693 - acc: 0.4187 - val_loss: 9.7307 - val_acc: 0.3401
Epoch 13/20
Epoch 00012: val_loss did not improve
1s - loss: 9.1400 - acc: 0.4265 - val_loss: 9.7481 - val_acc: 0.3509
Epoch 14/20
Epoch 00013: val_loss improved from 9.73069 to 9.72751, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.1157 - acc: 0.4271 - val_loss: 9.7275 - val_acc: 0.3389
Epoch 15/20
Epoch 00014: val_loss improved from 9.72751 to 9.64252, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 9.0092 - acc: 0.4301 - val_loss: 9.6425 - val_acc: 0.3557
Epoch 16/20
Epoch 00015: val_loss improved from 9.64252 to 9.51431, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 8.9015 - acc: 0.4364 - val_loss: 9.5143 - val_acc: 0.3641
Epoch 17/20
Epoch 00016: val_loss did not improve
2s - loss: 8.8412 - acc: 0.4433 - val_loss: 9.5502 - val_acc: 0.3533
Epoch 18/20
Epoch 00017: val_loss improved from 9.51431 to 9.49191, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 8.8211 - acc: 0.4472 - val_loss: 9.4919 - val_acc: 0.3545
Epoch 19/20
Epoch 00018: val_loss improved from 9.49191 to 9.31647, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 8.7725 - acc: 0.4482 - val_loss: 9.3165 - val_acc: 0.3641
Epoch 20/20
Epoch 00019: val_loss improved from 9.31647 to 9.31013, saving model to saved_models/weights.best.VGG16.hdf5
1s - loss: 8.5416 - acc: 0.4506 - val_loss: 9.3101 - val_acc: 0.3533
Out[66]:
<keras.callbacks.History at 0x1dc985fdbe0>

Load the Model with the Best Validation Loss

In [67]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

In [68]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 36.2440%

Predict Dog Breed with the Model

In [70]:
from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model, with the highest probability
    return dog_names[np.argmax(predicted_vector)]

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

I will now use transfer learning to create a CNN that can identify dog breed from images. The CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, I use the bottleneck features from a different pre-trained model. Use these pre-computed the features for all of the networks that are currently available in Keras:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [61]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_Resnet50 = bottleneck_features['train']
valid_Resnet50 = bottleneck_features['valid']
test_Resnet50 = bottleneck_features['test']

Model Architecture

Create a CNN to classify dog breed. At the end of the code cell block, summarize the layers of the model by executing the line:

    <model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer: From the bottleneck input, I add a globalaveragepoolinglayer to reduce the dimension of the input data. Then I add a fully connected dense layer in the end with 133 output nodes and "softmax" to generate the probability of each breed. This transfer learning works because the training label is very similar to the ImageNet data (exactly the same) and the training size of the data is relatively small. To avoid overfitting, we can keep the weights of all hidden layers unchanged and only train the fully connected output layer.

In [62]:
### TODO: Define your architecture.
Resnet50_model = Sequential()
Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))
Resnet50_model.add(Dense(133, activation='softmax'))

Resnet50_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 133)               272517    
=================================================================
Total params: 272,517.0
Trainable params: 272,517.0
Non-trainable params: 0.0
_________________________________________________________________

Compile the Model

In [63]:
### TODO: Compile the model.
Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

Train the model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

In [64]:
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5', 
                               verbose=1, save_best_only=True)

Resnet50_model.fit(train_Resnet50, train_targets, 
          validation_data=(valid_Resnet50, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=2)
Train on 6680 samples, validate on 835 samples
Epoch 1/20
Epoch 00000: val_loss improved from inf to 0.81883, saving model to saved_models/weights.best.Resnet50.hdf5
6s - loss: 1.6453 - acc: 0.5942 - val_loss: 0.8188 - val_acc: 0.7521
Epoch 2/20
Epoch 00001: val_loss improved from 0.81883 to 0.73216, saving model to saved_models/weights.best.Resnet50.hdf5
2s - loss: 0.4463 - acc: 0.8572 - val_loss: 0.7322 - val_acc: 0.7749
Epoch 3/20
Epoch 00002: val_loss improved from 0.73216 to 0.67093, saving model to saved_models/weights.best.Resnet50.hdf5
2s - loss: 0.2674 - acc: 0.9120 - val_loss: 0.6709 - val_acc: 0.7964
Epoch 4/20
Epoch 00003: val_loss improved from 0.67093 to 0.62920, saving model to saved_models/weights.best.Resnet50.hdf5
2s - loss: 0.1761 - acc: 0.9455 - val_loss: 0.6292 - val_acc: 0.8108
Epoch 5/20
Epoch 00004: val_loss did not improve
2s - loss: 0.1222 - acc: 0.9612 - val_loss: 0.6693 - val_acc: 0.8228
Epoch 6/20
Epoch 00005: val_loss did not improve
2s - loss: 0.0901 - acc: 0.9719 - val_loss: 0.6852 - val_acc: 0.8096
Epoch 7/20
Epoch 00006: val_loss did not improve
2s - loss: 0.0641 - acc: 0.9808 - val_loss: 0.6844 - val_acc: 0.8311
Epoch 8/20
Epoch 00007: val_loss did not improve
2s - loss: 0.0460 - acc: 0.9868 - val_loss: 0.7108 - val_acc: 0.8192
Epoch 9/20
Epoch 00008: val_loss did not improve
2s - loss: 0.0366 - acc: 0.9903 - val_loss: 0.7299 - val_acc: 0.8275
Epoch 10/20
Epoch 00009: val_loss did not improve
3s - loss: 0.0285 - acc: 0.9922 - val_loss: 0.8186 - val_acc: 0.8108
Epoch 11/20
Epoch 00010: val_loss did not improve
3s - loss: 0.0191 - acc: 0.9951 - val_loss: 0.7975 - val_acc: 0.8240
Epoch 12/20
Epoch 00011: val_loss did not improve
3s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.7888 - val_acc: 0.8240
Epoch 13/20
Epoch 00012: val_loss did not improve
2s - loss: 0.0131 - acc: 0.9967 - val_loss: 0.8074 - val_acc: 0.8323
Epoch 14/20
Epoch 00013: val_loss did not improve
2s - loss: 0.0109 - acc: 0.9976 - val_loss: 0.8227 - val_acc: 0.8180
Epoch 15/20
Epoch 00014: val_loss did not improve
2s - loss: 0.0098 - acc: 0.9975 - val_loss: 0.8469 - val_acc: 0.8228
Epoch 16/20
Epoch 00015: val_loss did not improve
3s - loss: 0.0091 - acc: 0.9979 - val_loss: 0.8601 - val_acc: 0.8132
Epoch 17/20
Epoch 00016: val_loss did not improve
2s - loss: 0.0087 - acc: 0.9979 - val_loss: 0.9059 - val_acc: 0.8335
Epoch 18/20
Epoch 00017: val_loss did not improve
2s - loss: 0.0071 - acc: 0.9984 - val_loss: 0.8953 - val_acc: 0.8287
Epoch 19/20
Epoch 00018: val_loss did not improve
2s - loss: 0.0071 - acc: 0.9981 - val_loss: 0.9261 - val_acc: 0.8347
Epoch 20/20
Epoch 00019: val_loss did not improve
2s - loss: 0.0052 - acc: 0.9985 - val_loss: 0.9454 - val_acc: 0.8251
Out[64]:
<keras.callbacks.History at 0x1f7e3809908>

Load the Model with the Best Validation Loss

In [65]:
### TODO: Load the model weights with the best validation loss.
Resnet50_model.load_weights('saved_models/weights.best.Resnet50.hdf5')

Test the Model

Ensure that the test accuracy is greater than 60%.

In [66]:
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]

# report test accuracy
test_accuracy = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 79.9043%

Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by the model.

Similar to the analogous function in Step 5, the function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to my chosen CNN architecture, I need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [67]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
from extract_bottleneck_features import *

def Resnet50_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = Resnet50_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model, with the highest probability
    return dog_names[np.argmax(predicted_vector)]

Step 6: Write Own Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

Write Own Algorithm

In [31]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def detect(img_path):
    if dog_detector(img_path): # dog is detected
        pred = Resnet50_predict_breed(img_path)
        print ("Hello, dog!")
        img = cv2.imread(img_path)
        cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        plt.show()
        print ("You are a ...\n", pred)
    elif face_detector(img_path): #human detected
        pred = Resnet50_predict_breed(img_path)
        print ("Hello, human!")
        img = cv2.imread(img_path)
        cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        plt.show()
        print ("You look like a ...\n", pred)
    else: # not human or dog
        print ("Error: neither dog nor human is detected.")   
        img = cv2.imread(img_path)
        cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        plt.imshow(cv_rgb)
        plt.show()

Step 7: Test Own Algorithm

What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

Test My Own Algorithm on Sample Images!

Test the algorithm at least six images. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: The algorithm predicts me being a Lowchen, very friendly and intelligent dog. The output is better than I expected. It can well distinguish human, dog, and other images. It can also tell cats from dogs. However, it cannot detect mutt, a mixture of different breed. To further improve the algorithm, I can do the following

  1. if a dog appears at least 40% of two breeds, return "mutt" instead of the highest possible breed
  2. use data augmentation to generate more training data via random transformation
  3. try different transfer learning model and dense network architecture
In [104]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
In [110]:
detect('mytest/mutt.jpg')
Hello, dog!
You are a ...
 Anatolian_shepherd_dog
In [84]:
detect('mytest/pomsky_8.jpg')
Hello, dog!
You are a ...
 Alaskan_malamute
In [85]:
detect('mytest/disney.jpg')
Hello, dog!
You are a ...
 Basset_hound
In [79]:
detect('mytest/HP_PCC_md_0130_cat53.jpg')
Error: neither dog nor human is detected.
In [114]:
detect('mytest/IMG_2208.jpg')
Error: neither dog nor human is detected.
In [81]:
detect('mytest/emma-stone-aloha-miscast.jpg')
Hello, human!
You look like a ...
 Maltese
In [83]:
detect('mytest/pharah.jpg')
Hello, human!
You look like a ...
 German_pinscher